Friday, May 29, 2020

About the Lancet paper on hydroxychloroquine and COVID-19. Page 2

Copyright 2020 Robert Clark

 In the blog post,  "About the Lancet paper on hydroxychloroquine and COVID-19", I discussed a letter I sent to the authors of the recent Lancet paper:

Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis.
Prof Mandeep R Mehra, MD  Sapan S Desai, MD  Prof Frank Ruschitzka, MD  Amit N Patel, MD
Published: May 22, 2020 DOI:

 The issue I had was with the higher number of patients placed on ventilators among the HCQ group compared to the non-HCQ, over twice as many. COVID-19 patients placed on ventilators have poor outcomes. In New York for example, only 20% of them survive it. This would skew the mortality numbers for the HCQ group towards higher mortality.

 So the question then was this: was the higher number of intubated patients in the HCQ group seen in this study because of the HCQ or was it because even after the authors applied methods to compensate for HCQ being given more to the sicker patients, this bias against HCQ still remained in the data?

 I'm inclined to believe the latter because while HCQ had been connected to heart problems it was not known to cause breathing problems.

 If this is the case then how the authors conducted their statistical adjustments to the data becomes extremely important. I advised the authors to release their data and describe their procedures for how they compensated for HCQ being prescribed to the more sicker patients to begin with.

 The authors releasing their data and procedures becomes even more important because of this recent news report:

Questions raised over hydroxychloroquine study which caused WHO to halt trials for Covid-19.

 The company that provided the data for the Lancet paper has been accused of falsifying data and Australia has denied ever supplying them with the information they claimed comes from Australian hospitals.

 This company, Surgisphere, has refused to hand over their data. In view of the importance of this paper with randomized, controlled trials that could have finally determined the validity of HCQ’s effectiveness being cancelled, the authors should be required to either hand over their data or withdraw their paper.

  Because of the controversy and doubt about how the data in such studies that are non-randomized are adjusted to mimic randomized studies, I recommend also the simplified approach I discussed in the prior post mentioned above. It's advantages are its transparency and simplicity. Everyone can see how the numbers are being developed at every step of the process, and anyone with just a hand-calculator can do the calculation themselves, not needing advanced statistical packages to do it.

 Letter to the authors of the Lancet paper:

Re: About your article on HCQ and COVID-19.

Robert G Clark
Wed 5/27/2020 12:12 AM

  •  Mehra, Mandeep R.,M.D. 
Thanks for taking the time to respond, Dr. Mehra. Being intubated is a huge factor towards mortality for COVID-19 patients. In New York, only 20% of intubated COVID-19 patients survive:

 So it is a key question of whether the HCQ caused the intubation. I’m inclined to think no since HCQ while it was previously known to cause heart problems was not previously known to cause breathing problems. But in view of the questions importance, this is something that should be investigated by interviewing the doctors who treated these intubated patients. Prior to being put on the ventilator was their patients condition so poor that it was to be expected for the intubation to occur whether or not they took HCQ?

 Note then since COVID-19 affects the heart also this would explain the higher number of heart problems with the HCQ group if they were more greatly represented among the intubated patients.

 In fact, I would also expect for example there would be a higher number of strokes among the HCQ group in your study: the intubated patients being in worse condition would skew those numbers up as well for the HCQ group.

 So think of any life threatening medical condition that is known to be caused by COVID-19 but is not known to be caused by HCQ. I would imagine you will find those conditions will still predominate in the HCQ group even after you made your adjustments for HCQ being given to sicker patients.

 This uncertainty on the underlying cause of the higher mortality in the HCQ group is why I would like to see my simplified approach to equalizing between the HCQ and non-HCQ groups risk factors be implemented. 

 Because of the controversy of the issue, many people will have doubts about the conclusions when all they are given are the “adjusted” numbers not the original numbers.

 Furthermore, the simplified approach can be followed every step of the way to see the calculations are being done appropriately and can be done by anyone with just a high school understanding of proportions and percentages. You don’t need advanced statistical knowledge or advanced statistical computer packages to do it. The calculations can even be done simply by hand. 

 To this end I again request that the original numbers be provided so pretty much anyone can make their own calculations for equalizing between the HCQ and non-HCQ groups.

 Also, remember the importance of having the original numbers is not just for doing the equalizing calculation. It may very well be that certain risk factors are better addressed by HCQ. You can imagine a scenario, for example, that people with hypertension have better outcome on HCQ. You can only make this comparison to the non-HCQ case if you have the original numbers.

 Thank You,

    Robert Clark

Robert Clark
Dept. of Mathematics
Widener University
One University Place
Chester, PA 19013 USA

Thursday, May 28, 2020

About the Lancet paper on hydroxychloroquine and COVID-19.

Copyright 2020 Robert Clark

 Attached below is an email I sent to the authors of the recent Lancet paper on HCQ and COVID-19:

Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis.
Prof Mandeep R Mehra, MD  Sapan S Desai, MD  Prof Frank Ruschitzka, MD  Amit N Patel, MD
Published: May 22, 2020DOI:

 First of all, the paper asserted that it only included patients that received the medications within 48 hours of diagnosis. This unfortunately gave the impression that this meant it was a case of "early treatment" with HCQ.

 The proponents of HCQ have argued it must be given early to prevent progression to serious illness, where it is much less effective. In fact, they argue it should be given on first sign of symptoms, even if before a positive test result comes back. The reason is it may take up to a week for the test results to come back, and over that time the virus is getting worse.

 Then this study really wasn’t early treatment despite what was said in the paper for reasons explained here:

James Todaro, MD @JamesTodaroMD May 22

The Lancet study gives the appearance of "early treatment" w/ HCQ, but this is NOT the case.

Symptom onset to hospitalization = 7 days
Hospitalization to diagnosis = 2 days
Diagnosis to treatment = 1-2 days

Time from symptoms to HCQ treatment = 10+ days

  Also, the cases in the Lancet article were those diagnosed from Dec. 2019 to mid-April. During this earlier time, it took several days to get test results back sometimes as much as a week:

 So for most cases in the Lancet study the actual time between symptom onset to treatment was likely greater than two weeks.

 The authors of the Lancet article have acknowledged also the study was only concerned with hospitalized patients, and people with COVID-19 symptoms only require hospitalization when their symptoms have progressed to a severe level.

 For this reason, the authors also have stated the results of the study should not be regarded as applicable to use of HCQ in an early, outpatient treatment format.

 Below, my letter to the authors:

About your article on HCQ and COVID-19.

Robert G Clark
Mon 5/25/2020 10:42 AM

 Hello, Dr. Mehra. I was very interested to read your article on HCQ treatments for COVID-19. The problem with such studies, absent of RCT’s, is the sicker patients get the test drug and the healthier patients do not. This skews the mortality in the test group to be worse and the mortality in the control group to be better.

 I just saw a problem with how you tried to account for that:

Hydroxychloroquine: When medical science starts to look like political science.
May 23, 2020
 It discusses the attached table from your report. This shows that the number of HCQ patients on ventilators was over twice the number as the controls. This shows your study didn’t sufficiently match for severity of disease between the two groups. Being on a ventilator is of course is a high indicator of a poor final outcome. Also, for severe cases as well of COVID-19 it is known the disease can causes heart problems, which also explains the higher number of heart problems seen in the HCQ group.

 So tell me if this is a reasonable way to instead approach the question of equalizing the HCQ and control groups. Lets first look at all the patients in toto. I assume the medical records were taken before being assigned the medication. Look at the proportion of cases for each category such as hypertension for all the patients. Then scale up or down the number of cases in the test and control groups for each category so the proportions match those for all the patients.

 Now, take for the number of deaths for each category in the test and control groups to make the mortality ratios match those observed in the original study data. Note this may mean the numbers won’t match what the actual numbers are for each group, but what we are trying to do is equalize the proportions for the test and control groups so we can get a fairer comparison between the groups.

 In this way you have for both the test group and the control group equal proportions of cases with the high risk factors and you have the mortalities estimated from the original data found.

 That’s for a first level analysis. But there is a problem here in that we know for patients that have multiple risk factors their mortality would be worse. But we are looking at the risk factors only one at a time. To get an even more accurate analysis we break up all the cases into further subcategories when patients had two or more of the risk factors. We then make the proportion of cases in the test and control groups match these proportions and again make the mortality ratios in the subcategories match those in the original study.

 So can you provide the raw numbers, before the propensity adjustments, say, as a supplement to your report, so we can calculate these ratios ourselves?

  Thank You,

    Robert Clark

Robert Clark
Dept. of Mathematics
Widener University
One University Place
Chester, PA 19013 USA

Thursday, May 14, 2020

Success in Italy reported in early treatment of COVID-19 using hydroxychloroquine.

Copyright 2020 Robert Clark

 It is becoming abundant clear that antiviral treatments for COVID-19 are most effective when given early. Drs. Raoult and Zelenko have stated this repeatedly in regards to HCQ yet in the U.S at least studies keep being done with HCQ on patients who are already under severe disease condition.

The importance of early treatment with HCQ is illustrated by a recent news article from Italy.
From Google Translate:

SCIENCE Coronavirus - From North to South 1039 patients treated with hydroxychloroquine at home. The point on experimentation: "Collapse of hospitalizations".
"I am a doctor and, positive for Covid19 , I immediately took hydroxychloroquine : in 3-4 days the fever and other symptoms disappeared ". This is how Paola Varese , head of cancer medicine at the Ovada Hospital in Piedmont , begins . "I applied the same protocol on myself that I planned for 276 patients at home," continues Varese , stressing that "timely intervention by family doctors in patients' homes is essential, with hydroxychloroquine associated with heparin (and if necessary the ' antibiotic ). It is presumable - he says - that the collapse of the hospitalization is due to the immediate use of the drug : we only had 7 hospitalizations: according to the projected expectations of the ISS we should have had 55 ".

 So the hospitalization rate dropped by a factor of 8 for the cases seen by this doctor. This fact would be extremely important to know in infection clusters such as New York, which was close to being overwhelmed by the number of hospitalized cases.
 According to this article, in New York only 20% of COVID-19 patients put on ventilators survive, so an 80% mortality rate for those on ventilators:

A bridge between life and death: Most COVID-19 patients put on ventilators will not survive
John Bacon

 Imagine if the Italian numbers of HCQ prevention of hospitalizations held true in New York. The number of hospitalizations and subsequent deaths could have been cut by a factor of 8.
 Also important is it could have dropped the death rate by an larger factor than 8.
 The death rate might have dropped by an even larger number than just by a factor of 8 because it seems likely that for those cases that were admitted to hospital the severity would also have been reduced. Indeed that was what was noted by doctors quoted in the Italian article.
 This fact about the reduced hospitalizations also shows why even small studies can be important. The HCQ studies done so far have been criticized because they were small in number, or wasn’t randomized, or without controls. But imagine a situation like in New York where a hospital may have seen in the range of 200 HCQ admissions in a week, imagine that being dropped to in the range of only 25. The doctors in that hospital wouldn’t care that this is only a small sample or it wasn’t randomized or didn’t have a control group. They would only care their case load was radically reduced, which allows them also to focus more on the patients they already have.
 So you don’t need to have a randomized controlled double-blind trial with thousands of cases costing tens of millions of dollars and taking months to complete. If every hospital that tried the policy of giving HCQ once someone tested positive prior to severe symptoms or any symptoms appearing, and all those hospitals within a matter of days saw their new cases dropped by a factor of 8, that would be powerful evidence for the effectiveness of HCQ.
  Another doctor in Italy even advises giving HCQ on the first signs of symptoms even before a positive test comes back. The reason is it takes days to get the test results back during which time the disease is getting worse. So give the medication as soon as possible:

Coronavirus, the method that avoids the massacre: "No patient has died"
Piacenza flooded with infections. The first dead. Then the idea of ​​Dr. Luigi Cavanna: home-to-home care. "So patients heal"
Giuseppe De Lorenzo Andrea Indini -Fri, 08/05/2020 - 07:55

 This shows another important fact about the drop in hospitalizations using HCQ early. A criticism of the small studies with HCQ is that most patients get better anyway so you would need a large sample size to show a statistically important difference in death rates when using HCQ.

But because the drop in hospitalizations is by such a large factor over not using early HCQ you can see easily the effect of using the HCQ.

 Moreover, since most patients who will need hospitalizations develop the serious symptoms within a matter of days, doctors in hospitals could see the reduction in the number of admissions within a matter of days. This is a key distinction over just looking at death rates because a patient could be treated for weeks in hospital before it is finally known if he will fully recover or not.
 It should be noted though that not all the Italian doctors quoted in the article agree with the effectiveness of HCQ in early treatment. From the English translation:
However, some of the scientific community remains cautious. According to the director of the infectious diseases department of the "Sacco" of Milan, Massimo Galli , "this drug (hydroxychloroquine) is used as an antimalarial prophylaxis , but it is not useful as a prophylaxis against this virus , and can cause serious damage to those suffering from heart and those with favism ”.
 It is not clear though whether or not Dr. Galli has treated patients early using HCQ so that he could determine whether or not it reduced hospitalizations. By the way, the “favism” he mentions is a condition that occurs at a higher rate among people of Mediterranean descent that he says HCQ could have bad side effect on. HCQ would have to be given with knowledge of the patients prior health history.
 Also, not all doctors quoted in the article saw the large factor of 8 drop in hospitalizations. Of the doctors quoted using HCQ early, one saw a drop by a factor of 6 and two others saw a reduction by a factor of 2 in hospitalizations. The variation might be due to how early the HCQ is given after infection.

Note: there is a problem with the google translation with the Italian word “sfebbrata”. The original Italian passage is this:
Se prima del trattamento si avevano alterazioni della temperatura fino a 10-12 giorni, dopo l’introduzione sistematica di idrossiclorochina, il 75% delle persone si è sfebbrata entro il 4° giorno e l’85% entro l’8° giorno”.
Google translate gives this in English as:
If before the treatment there were changes in temperature for up to 10-12 days, after the systematic introduction of hydroxychloroquine , 75% of the people choked by the 4th day and 85% by the 8th day “.
The English word “choked” doesn’t fit here since the doctor being quoted is describing positive benefits of HCQ. I did a web search and a better meaning here should be “fever broke”. This fits in the sentence since it is describing high temperatures.
Another minor issue is this with this passage in the original Italian:
“Tra i 169 pazienti trattati non vi è stato nessun decesso. Il 7% dei trattati è stato ricoverato, ma nessuno ha sviluppato complicanze gravi, né ha avuto alcun effetto collaterale durante il trattamento”.
Google translate gives this as:
“Among the 169 patients treated there was no death . 7% of the treaties were hospitalized , but none developed serious complications , nor had any side effects during treatment "..
It would be easy to misread the google translation of the hospitalizations as “.7%”. Instead of the correct 7%. But this is because the “7” comes after the period of the prior sentence.

  Bob Clark

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